DeepHGNN: A Novel Deep Hypergraph Neural Network

LIN Jingjing; YE Zhonglin; ZHAO Haixing; FANG Lusheng

doi:10.1049/cje.2021.00.108

Volume 31 Issue 5

Sep. 2022

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LIN Jingjing, YE Zhonglin, ZHAO Haixing, et al., “DeepHGNN: A Novel Deep Hypergraph Neural Network,” Chinese Journal of Electronics, vol. 31, no. 5, pp. 958-968, 2022, doi: 10.1049/cje.2021.00.108

Citation:

LIN Jingjing, YE Zhonglin, ZHAO Haixing, et al., “DeepHGNN: A Novel Deep Hypergraph Neural Network,” Chinese Journal of Electronics, vol. 31, no. 5, pp. 958-968, 2022, doi: 10.1049/cje.2021.00.108

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DeepHGNN: A Novel Deep Hypergraph Neural Network

doi: 10.1049/cje.2021.00.108

LIN Jingjing^{1, 2, 3, 4
,},
YE Zhonglin^{1, 2, 3
,},
ZHAO Haixing^{1, 2, 3
,},
FANG Lusheng^{2, 3
,}

1.
College of Computer, Qinghai Normal University, Xining 810008, China
2.
The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining 810008, China
3.
Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province, Xining 810008, China
4.
Xining Urban Vocational & Technical College, Xining 810003, China

Funds: This work was supported by the National Key R&D Program of China (2020YFC1523300), the Youth Program of Natural Science Foundation of Qinghai Province (2021-ZJ-946Q), and the Middle-Youth Program of Natural Science Foundation of Qinghai Normal University (2020QZR007).

More Information

Author Bio:
was born in 1986. She received the M.S. degree from Chengdu University of Technology in 2015. She is currently pursuing the Ph.D. degree at the School of Computer in Qinghai Normal University. Her research interests include graph neural networks and hypergraph neural networks.(Email: ljj_mail@126.com)

was born in 1989. He received the B.S. degree from Sichuan University in 2012. He received the M.S. degree from Southwest Jiaotong University in 2016, and the Ph.D. degree at the School of Computer Science in Shaanxi Normal University in 2019. His research interests include graph neural networks, knowledge extraction, and network representation learning. (Email: zhonglin_ye@foxmail.com)

(corresponding author) was born in 1969. He received his Doctor of Engineering Degree from the School of Computer Science in Northwestern Polytechnical University in 2004. He also received his Doctor of Science Degree from University of Twente in Holland. He is a Professor and part-time Professor at Qinghai Normal University and Shaanxi Normal University, respectively. He is a Director of Changjiang Scholars and Innovative Research Team in University. He is also the Syndic of Operations Research Society, Combinatorics and Graph Theory Society in China. His research interests include complex network, graph neural networks, machine translation, hypergraph theory, network reliability, etc. (Email: h.x.zhao@163.com)

was born in 1996. He received a bachelor’s degree in information and computing science from Dezhou College. He is now a graduate student in the School of Mathematics and Statistics of Qinghai Normal University. His research interests include graph neural networks and hypergraph coloring entropy. (Email: lusheng_fang@126.com)
Received Date: 2021-03-29
Accepted Date: 2021-06-25

Available Online: 2021-11-02

Publish Date: 2022-09-05

Abstract

Abstract

With the development of deep learning, graph neural networks (GNNs) have yielded substantial results in various application fields. GNNs mainly consider the pair-wise connections and deal with graph-structured data. In many real-world networks, the relations between objects are complex and go beyond pair-wise. Hypergraph is a flexible modeling tool to describe intricate and higher-order correlations. The researchers have been concerned how to develop hypergraph-based neural network model. The existing hypergraph neural networks show better performance in node classification tasks and so on, while they are shallow network because of over-smoothing, over-fitting and gradient vanishment. To tackle these issues, we present a novel deep hypergraph neural network (DeepHGNN). We design DeepHGNN by using the technologies of sampling hyperedge, residual connection and identity mapping, residual connection and identity mapping bring from graph convolutional neural networks. We evaluate DeepHGNN on two visual object datasets. The experiments show the positive effects of DeepHGNN, and it works better in visual object classification tasks.
- Deep neural networks,
- Graph neural network,
- Hypergraph neural network,
- Deep hypergraph neural network

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References(47)

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